US7184846B2 - Systems and methods for providing optimal light-CO2 combinations for plant production - Google Patents

Systems and methods for providing optimal light-CO2 combinations for plant production Download PDF

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US7184846B2
US7184846B2 US10/976,958 US97695804A US7184846B2 US 7184846 B2 US7184846 B2 US 7184846B2 US 97695804 A US97695804 A US 97695804A US 7184846 B2 US7184846 B2 US 7184846B2
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resource
cost
control system
time period
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US20050252078A1 (en
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Louis D. Albright
Konstantinos Ferentinos
Ido Seginer
David S. de Villiers
Jeffrey W. Ho
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Cornell Research Foundation Inc
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
    • A01G9/18—Greenhouses for treating plants with carbon dioxide or the like
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • A01G7/02—Treatment of plants with carbon dioxide
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric

Definitions

  • the present invention relates generally to plant production systems and more particularly to controlling resources related to plant production
  • plants In order for plants to grow, they need various resources. For example, plants require light as part of their photosynthesis process. Plant production may be enhanced by addition of supplemental lighting, but this comes at a cost. Similarly, plant production may be enhanced by the addition of supplemental CO 2 , but this too comes at a cost.
  • the present invention provides optimum control of multiple resources involved in plant production.
  • the present invention provides computerized control systems including a processor and resource controllers that control plant growth by adjusting the amounts of plant growth resources provided to a plant.
  • the cost of each resource is taken into account during calculations performed by the processor to achieve a desired plant production rate.
  • the cost of each resource may vary based upon the time period during which the resource is to be added.
  • the presence of the resources e.g., lighting or carbon dioxide
  • the resource controllers may then cause the calculated amounts of resource to be physically implemented.
  • the present invention provides methods of controlling resources for growing a plant that are preferably, but not exclusively, implemented in a computerized environment.
  • the method involves receiving a desired plant production rate related to a number of plant growth resources and costs associated with the resources that may vary with a resource cost time period during which the resources are to be expended, and determining based on the resource cost time period respective amounts of the resources that should be expended during the time period to achieve the desired plant production rate.
  • the determinations may be made periodically for a plurality of time intervals within each resource cost time period and/or upon a change to a differing resource cost time period.
  • the resources comprise electricity for a lighting system and carbon dioxide (CO 2 ).
  • the resource cost time period may be defined as peak periods and non-peak periods having different costs for a resource.
  • One or more of the resources may be applied to supplement a naturally occurring component of the resource (e.g., sunlight) that may also be varying, according to a natural resource time period (e.g., daytime and nighttime) or due to some loss of resource, such as CO 2 decay from ventilation or infiltration of a greenhouse.
  • the systems and methods of the present invention take into account in the determination of the amounts of the resources to be expended in subsequent time intervals the proportional plant growth that has been achieved up to the point of the determination. Predictions of environmental conditions over subsequent time intervals that affect the plant production rate may also be calculated, including outdoor air temperatures, solar intensity, and ventilation rates from a greenhouse encompassing the plant.
  • Simulations are presented below of a computer algorithm that considers a range of light and CO 2 control combinations for the next decision period (time interval), estimates the ventilation rate expected, and finds the optimum (lowest cost) combination of resources for achieving the desired plant production rate.
  • FIG. 1 is a block diagram of a hardware and operating environment in which different embodiments of the invention can be practiced;
  • FIG. 2 is a diagram providing further details of a host computer environment according to an embodiment of the invention.
  • FIGS. 3A–3D are flowcharts illustrating methods for controlling plant production resources according to an embodiment of the invention.
  • FIG. 4 is a diagram illustrating exemplary time periods used in various embodiments of the invention.
  • FIG. 5 is a schematic presentation of an L-X plane according to an embodiment of the invention.
  • FIG. 6 is an illustration of optimal CO 2 concentration as a function of available natural light for several ventilation rates
  • FIG. 7 is an illustration of the cost of the solutions shown in FIG. 6 ;
  • FIG. 8 is an graph of daily PAR integral and CO 2 combinations leading to shoot fresh mass of 190 g lettuce, cv. Vivaldi, 35 days after seeding;
  • FIG. 9 is a graph of errors in predicting outdoor hourly air temperatures using a second order polynomial based on the current and two previous hourly air temperature readings in accordance with a method of the invention.
  • FIG. 10 is an illustration of the elements of a greenhouse thermal model in accordance with the present invention.
  • FIG. 11 is a graph of outdoor air temperature prediction accuracy according to a method of the invention as a function of time of day.
  • a first section describes a hardware and software environment according to embodiments of the invention.
  • a second section describes a method according to an embodiment of the invention.
  • a third section provides a description of various parameters and formulas used in embodiments of the invention in which light and carbon dioxide resources are managed in a manner to minimize overall operating cost, and a general analysis of the models presented follows in a fourth section along with a discussion of the equivalence of instantaneous photosynthesis and the photosynthesis curves as found in the Both, et al. (2000) reference mentioned above. Exemplary simulated results are provided in a fifth section for practicing methods according to the invention, and conclusions are presented in the final section.
  • FIG. 1 is a block diagram of a hardware and operating environment in which different embodiments of the invention can be practiced.
  • environment 100 resides in a greenhouse and includes a computer 102 , a database 120 , resource controllers 110 and 112 operable to control resources 106 and 108 respectively.
  • Resources 108 are directed to the production of plants 104 .
  • Computer 102 may be any general purpose computer, including personal computers, programmable logic controllers, server computers, mainframe computers, laptop computers, personal digital assistants or combinations of the above distributed in a network environment. Further details regarding computer 102 are provided below with reference to FIG. 2 .
  • Database 120 provides storage for programs and data used by computer 102 .
  • Database 120 may be a disk resident database, or database 120 may be a memory resident database. The invention is not limited to a particular database type.
  • database 120 maintains information regarding first resource 106 and second resource 108 . This information may include cost data and time period data that may be associated with the cost data.
  • First resource 106 and second resource 108 are resources directed to the production of plants 104 .
  • first resource 106 comprises electricity that controls supplemental lighting used to produce plants 104 .
  • second resource 108 comprises supplemental carbon dioxide (CO 2 ) that may be administered to produce plants 104 .
  • CO 2 supplemental carbon dioxide
  • the invention is not limited to a particular resource and alternative resources may be used in addition to or instead of supplemental light and CO 2 .
  • First resource controller 110 is communicably coupled to computer 102 and is used to control the administration of first resource 106 .
  • first resource controller 110 is operable to control whether supplemental lighting is turned on or off.
  • the supplemental lighting is either all on or all off.
  • various combinations of lights may be turned on and off to achieve a desired lighting amount.
  • dimming ballasts may be used in conjunction with the supplemental lighting to achieve a desired lighting amount.
  • Second resource controller 108 controls the output of the second resource 108 .
  • second resource controller 112 controls the output of CO 2 into the plant's environment.
  • Some embodiments of the invention include a monitor 114 that monitors the ventilation rate in environment 100 .
  • CO 2 is used as a tracer gas to monitor the ventilation rate in environment 100 .
  • the use of CO 2 as a tracer gas is known in the art.
  • FIG. 2 is a diagram providing further details of a host computer 102 in conjunction with which embodiments of the invention may be practiced.
  • the description of FIG. 2 is intended to provide a brief, general description of suitable computer hardware and a suitable computing environment in conjunction with which the invention may be implemented.
  • the invention is described in the general context of computer-executable instructions, such as program modules, being executed by a computer, such as a personal computer or a server computer.
  • program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types.
  • the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like.
  • the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote memory storage devices.
  • the computing system 200 includes a processor.
  • the invention can be implemented on computers based upon microprocessors such as the PENTIUM® family of microprocessors manufactured by the Intel Corporation, the MIPS® family of microprocessors from the Silicon Graphics Corporation, the POWERPC® family of microprocessors from both the Motorola Corporation and the IBM Corporation, the PRECISION ARCHITECTURE® family of microprocessors from the Hewlett-Packard Company, the SPARC® family of microprocessors from the Sun Microsystems Corporation, or the ALPHA® family of microprocessors from the Compaq Computer Corporation.
  • Computing system 200 represents any personal computer, laptop, server, or even a battery-powered, pocket-sized, mobile computer known as a hand-held PC.
  • the computing system 200 includes system memory 213 (including read-only memory (ROM) 214 and random access memory (RAM) 215 ), which is connected to the processor 212 by a system data/address bus 216 .
  • ROM 214 represents any device that is primarily read-only including electrically erasable programmable read-only memory (EEPROM), flash memory, etc.
  • RAM 215 represents any random access memory such as Synchronous Dynamic Random Access Memory.
  • input/output bus 218 is connected to the data/address bus 216 via bus controller 219 .
  • input/output bus 218 is implemented as a standard Peripheral Component Interconnect (PCI) bus.
  • PCI Peripheral Component Interconnect
  • the bus controller 219 examines all signals from the processor 212 to route the signals to the appropriate bus. Signals between the processor 212 and the system memory 213 are merely passed through the bus controller 219 . However, signals from the processor 212 intended for devices other than system memory 213 are routed onto the input/output bus 218 .
  • Various devices are connected to the input/output bus 218 including hard disk drive 220 , floppy drive 221 that is used to read floppy disk 251 , and optical drive 222 , such as a CD-ROM drive that is used to read an optical disk 252 .
  • the video display 224 or other kind of display device is connected to the input/output bus 218 via a video adapter 225 .
  • a user enters commands and information into the computing system 200 by using a keyboard 40 and/or pointing device, such as a mouse 42 , which are connected to bus 218 via input/output ports 228 .
  • a keyboard 40 and/or pointing device such as a mouse 42
  • Other types of pointing devices include track pads, track balls, joy sticks, data gloves, head trackers, and other devices suitable for positioning a cursor on the video display 224 .
  • the computing system 200 also includes a modem 229 . Although illustrated in FIG. 2 as external to the computing system 200 , those of ordinary skill in the art will quickly recognize that the modem 229 may also be internal to the computing system 200 .
  • the modem 229 is typically used to communicate over wide area networks (not shown), such as the global Internet.
  • the computing system may also contain a network interface card 53 , as is known in the art, for communication over a network.
  • Software applications 236 and data are typically stored via one of the memory storage devices, which may include the hard disk 220 , floppy disk 251 , CD-ROM 252 and are copied to RAM 215 for execution. In one embodiment, however, software applications 236 are stored in ROM 214 and are copied to RAM 215 for execution or are executed directly from ROM 214 .
  • the operating system 235 executes software applications 236 and carries out instructions issued by the user. For example, when the user wants to load a software application 236 , the operating system 235 interprets the instruction and causes the processor 212 to load software application 236 into RAM 215 from either the hard disk 220 or the optical disk 252 . Once software application 236 is loaded into the RAM 215 , it can be used by the processor 212 . In case of large software applications 236 , processor 212 loads various portions of program modules into RAM 215 as needed.
  • BIOS 217 The Basic Input/Output System (BIOS) 217 for the computing system 200 is stored in ROM 214 and is loaded into RAM 215 upon booting. Those skilled in the art will recognize that the BIOS 217 is a set of basic executable routines that have conventionally helped to transfer information between the computing resources within the computing system 200 . These low-level service routines are used by operating system 235 or other software applications 236 .
  • computing system 200 includes a registry (not shown) that is a system database that holds configuration information for computing system 200 .
  • a registry (not shown) that is a system database that holds configuration information for computing system 200 .
  • Windows® 95, Windows 98®, Windows® NT, Windows 2000® and Windows XP® by Microsoft maintain the registry in two hidden files, called USER.DAT and SYSTEM.DAT, located on a permanent storage device such as an internal disk.
  • FIGS. 3A–3D are flowcharts illustrating methods for controlling plant production resources according to an embodiment of the invention.
  • the methods to be performed by the operating environment constitute computer programs made up of computer-executable instructions.
  • the methods illustrated in FIGS. 3A–3D are inclusive of acts that may be taken by an operating environment such as described above.
  • FIG. 3A illustrates a method for controlling plant production wherein at least two resources are controlled.
  • the method begins by receiving a desired plant production rate (block 305 ).
  • the desired plant production rate may vary depending on the plant being grown.
  • the desired plant production rate is related lettuce production.
  • the desired plant production rate will depend on at least two resources.
  • a first resource comprises lighting and a second resource comprises CO 2 .
  • the first and second resource may include two components, a naturally occurring component and a supplemented component. For example, in some embodiments, naturally occurring lighting from the sun may be supplemented with artificial lighting, and ambient levels of CO 2 may be supplemented with purchased CO 2 .
  • the system receives a first cost associated with supplementing the first resource (block 310 ).
  • a resource will have a cost associated with it.
  • the cost of supplementing at least the first resource varies depending on a time period.
  • the time period comprises a peak time period and a non-peak time period. Additionally, there may be other time periods involved, such as a daytime period and a nighttime period.
  • the system receives a cost associated with supplementing the second resource (block 315 ).
  • the second resource also typically has a cost associated with it. This cost may or may not vary depending on the time period.
  • the system determines the amount of the first resource and the second resource that will be expended during the time period.
  • the cost data indicates that the amount of electricity that would need to be applied for supplemental lighting to achieve the desired growth rate is more expensive than the amount of CO 2 that would need to be applied to achieve the desired growth rate
  • the system will favor using supplemental CO 2 over supplemental lighting.
  • the cost of applying CO 2 is more expensive, the system will favor using supplemental lighting instead of supplemental CO 2 .
  • the effectiveness of expending a resource may be limited by external factors such as the naturally occurring amount of the resource. For example, it may not be cost-effective to provide supplemental lighting during daylight hours since the additional benefit provided by the supplemental lighting may be negligible in comparison with benefit obtained by the naturally occurring (and therefore cost-free) lighting. Similarly, if the amount of CO 2 naturally occurring in the environment is sufficiently high, it may not be cost-effective to introduce more CO 2 if the plants cannot absorb the additional amount, and/or if ventilation passes some upper limit, supplementing CO 2 becomes more expensive because of rapid losses out the greenhouse vents.
  • supplemental lighting may be necessary to provide supplemental lighting during daylight hours if the day is comparatively dark, which may occur for example on days during the winter.
  • the system generally predicts a sufficient light integral for the daylight hours using the equations defined below such that it would be unlikely to turn the supplemental lights on.
  • supplemental lighting is needed on particular days to reach the desired light integral, the lighting is typically done during the night to the extent possible, using the off-peak electric rates.
  • the system receives an indication that the time has moved into a different time period (block 325 ).
  • the system returns to block 310 in order to redetermine which resource is more cost-effective to achieve the desired production rate.
  • FIGS. 3B–3D illustrates a method executed by an operating environment according to embodiments of the invention, and provides further details on the method illustrated above in FIG. 3A where the first resource is light and the second resource is CO 2 .
  • a day or other time period is divided into intervals, and the tasks illustrated in FIGS. 3B–3D may be performed once during each interval.
  • the chosen interval is one hour.
  • the method begins by predicting lighting operation for the interval assuming ambient levels of CO 2 (block 332 ).
  • the prediction includes the control of the state of supplemental light and/or movable shades that determine the light within a greenhouse.
  • the system estimates the maximum air temperature over the interval (block 334 ).
  • the current temperature is obtained, for example from sensors communicably coupled to the system.
  • the current temperature may be obtained from other sources, such as sites on the Internet that provide local weather data.
  • the system estimates the maximum air temperature for the interval by taking the current interval reading, and the previous two interval readings, and fits a second order equation to them (examples include but are not limited to linear, polynomial, trigonometric, and spline functions) and extrapolates to the next time interval.
  • a linearized version of the second order equations may be used to estimate the maximum temperature over the interval. The present invention is not limited to any particular method for estimating the maximum temperature over the interval.
  • the system estimates the maximum solar insolation that will occur over the next interval (block 336 ). In some embodiments, this prediction may utilize the equations defined below along with solar insolation data accumulated since sunrise. The system also estimates the solar integral at sunset (block 338 ).
  • the system then checks to see whether the predicted photosynthetic active radiation (PAR) due to sunlight will be greater than the daily target required to meet desired plant production (block 340 ). If so, then no lighting supplementation is require (block 342 ). Shading control may be required to prevent oversaturation.
  • PAR photosynthetic active radiation
  • the system determines how to apply supplemental lighting and/or supplemental CO 2 .
  • the system uses the predicted lighting operation from block 332 and estimates the maximum ventilation for the next interval (block 344 ).
  • the predicted PAR and predicted maximum outdoor air temperature are used in an energy balance to predict the maximum ventilation rate during the next interval (to maintain the indoor temperature at the desired level). A further discussion of this technique is described below in Section 5. It is noted that in winter in cold climates, the desired rate will be zero and heating is needed. But there is typically always infiltration at some level.
  • the system calculates the proportion of desired growth already achieved for the day (block 350 ).
  • Each interval of the day since sunrise has its value of light integral for the past interval, and the average CO 2 concentration that existed for that interval.
  • the equation that relates light integral and CO 2 level to achieve the same growth is provided below.
  • the CO 2 level that existed can be used with that equation to determine the accompanying light integral target.
  • the actual light integral for the interval, divided by the accompanying light integral target, will be a fraction less than unity and represents the proportional growth that interval contributed to the day.
  • the system is then set to assume ambient levels of CO 2 (block 352 ).
  • the system then calculates the cost of providing supplemental CO 2 at the estimated ventilation rate (block 356 ).
  • the system calculates the proportional growth that would be achieved if the rest of the day is at ambient CO 2 , and the accompanying light integral that would be needed at ambient CO 2 using the current state of lighting control expected for the interval (block 360 ).
  • the system determines the proportional growth remaining for the rest of the day (block 362 ). From the proportional growth remaining, the system determines the PAR value needed for the rest of the day to achieve the remaining proportional growth (block 364 ).
  • the system determines whether supplemental light will be needed for any part of the rest of the day in order to achieve the desired PAR value (block 366 ). From this value, the system determines when the supplemental light would have to start to reach the integral target (block 368 ).
  • the system uses the on-peak or off-peak rates to determine the cost of lighting for the next interval and also determines the cost of supplemental CO 2 for the next interval (block 380 ).
  • the CO 2 concentration is also incremented to account for any supplemental CO 2 that may be added during the interval (block 382 ).
  • the system next determines if the lighting state changed (block 386 ). If the lighting state changes, the system then checks to see if the lighting state was changed in a previous iteration in this interval (block 392 ). If not, the system proceeds to block 344 to go through the loop again because the system started with the assumption the CO 2 was at the ambient level, and it will now not be at the ambient level due to the predicted supplementation of CO 2 . If the state did change in a previous iteration and the state changes again, the loop is indefinite. In some embodiments, supplemental lighting is forced on and no CO 2 is added (block 394 ).
  • the system proceeds to make a determination of the most cost effective light integral/CO 2 concentration combination based on the lighting cost (if any) plus the CO 2 cost based on the predicted ventilation rate (block 388 ).
  • the system chooses the combination with the lowest total cost of supplemental lighting and/or supplemental CO 2 . Lighting and CO 2 resources are then controlled in accordance with the chosen combination (block 390 .)
  • the system then waits until the beginning of the next interval (block 396 ), when the method illustrated in FIGS. 3B–3D may be repeated.
  • the method illustrated above is modified to account for CO 2 decay. For example, if the previous interval led to control with CO 2 above ambient levels, and the next interval suggests only ambient, the system takes into consideration the decay of CO 2 concentration, particularly if ventilation is not high. This is desirable because the decay of CO 2 can affect the calculation of potential growth. In some embodiments, a simple mixing model can be used to predict the CO 2 decay for the next hour, and beyond if ventilation is low enough.
  • the rate of adding CO 2 can be used to estimate the “current” ventilation rate, which can then be compared to the predicted to know whether it (the predicted) has been greatly exceeded.
  • the system detects if conditions are far from the predicted conditions (e.g., due to a sudden weather change), and forces the system to a default state.
  • the default state assumes ambient CO 2 .
  • FIG. 4 illustrates a set of exemplary time periods.
  • time periods P 1 –P 4 two defined by peak and non-peak electrical costs and two defined by daytime versus nighttime hours.
  • the daily cycle (with origin at sunset) may be divided into four periods as follows:
  • This section provides exemplary parameters used in various embodiments of the invention where the first and second resources comprise supplemental lighting and CO 2 .
  • This section also provides equations that may be used by various embodiments to assist in determining optimal combinations of supplemental light and CO 2 depending on a time period.
  • FIG. 5 is a schematic presentation of the L-X plane.
  • the square point is the candidate solution.
  • the horizontal dashed line is the natural level of CO 2 concentration.
  • the curve connects all the points that produce the desired rate of growth. L t ⁇ L n +L s and X t ⁇ X n +X s .
  • the length of the supplementary light period may be uniquely determined by the proposed solution and it is proportional to L s
  • the cost of the added light depends, however, on its timing.
  • the daily cycle (with origin at sunset) may be divided into four periods as discussed above with reference to FIG. 4 .
  • J depends on the times of turning the lights on and off. These are equivalent to t s and t o of FIG. 4 . Hence the search for the minimum of J may be carried out over the two dimensional [t s , t o ] space.
  • Period P 3 (daytime and off-peak electricity price) does not exist and the first period of choice is either P 2 (night and off-peak), or P 4 (day and on-peak). The last period is P 1 (night and on-peak).
  • P 2 night and off-peak
  • P 4 day and on-peak
  • P 1 night and on-peak
  • the solution with the + sign is a minimum, while the other solution is a maximum.
  • the discriminant is negative, there is no minimum, just an inflexion point, and the optimum is obtained on the border of the feasible region.
  • J 0 C L off ⁇ ( X n - c - b ⁇ ⁇ L n b ) + C X ⁇ m , [ 27 ] J 0 being independent of the ventilation rate.
  • Equation [28] X t ⁇ x, L t ⁇ I ⁇ i ⁇ , g ⁇ B + /A + , h ⁇ 1/ A + , t n ⁇ ; [29a] 4.2 Periods P 2 (Off-Peak; Night) and P 4 (On-Peak; Day)
  • L t - 1 2 ⁇ a ⁇ [ C L on C X ⁇ ⁇ ⁇ ⁇ Q ⁇ ( t n + t off ) + b ] , [ 32 ] which is a constant, independent of L n .
  • the luminaires may be also turned on during P 1 (on-peak price at night).
  • P 1 on-peak price at night.
  • Equation [37] For the quadratic approximation of ⁇ L t ⁇ (Equation [17]), Equation [37] becomes
  • Equation [40] is, again, a quadratic equation in L t , with L n and Q as parameters for a given day-length t n .
  • the solutions, if they exist, are calculated as
  • FIGS. 6 and 7 show results of the optimal CO 2 concentration and the associated cost function for the parameter values of Table 1 and for the lighting sequence P 2 , P 4 , P 1 .
  • a complete solution should also consider the sequence P 4 , P 2 , P 1 .
  • FIG. 6 shows the optimal CO 2 concentration as a function of available natural light integral (divided equally over 12 hours).
  • FIG. 6 illustrates optimal CO 2 concentration as a function of available natural light, for several ventilation rates, Q.
  • the curve on the right (No. 1; Ferentinos approx) connects all combinations of light flux and CO 2 concentration which produce the desired daily target.
  • the region between the parallel curves 1 and 2 provides supplementary light during the off-peak (P 2 ) period.
  • the region between curves 2 and 3 is for additional light provided during period P 4 (day-time and on-peak electricity price).
  • the region between curves 3 and 4 is for additional light during period P 1 (night-time and on-peak electricity price).
  • the particular values for X t (e.g. 360 ppm and 1600 ppm) are those used in an exemplary embodiment of the invention. No embodiment of the invention is limited to a particular lower or upper boundary for X t .
  • the solution behaves as follows: As the natural light integral diminishes, the solution point first climbs along the Ferentinos approximation by increasing the CO 2 concentration, while refraining from adding supplementary light. As the maximum permissible CO 2 concentration (1600 ppm) is reached, any further loss of natural light must be replaced by supplementary light during the off-peak (low electricity price) period. As the natural light diminishes further, CO 2 enrichment becomes less economic (due to longer enrichment time) and the optimal CO 2 concentration decreases. When the off-peak period is exhausted, increasing the CO 2 concentration becomes attractive again for a while, until supplementing with on-peak light becomes necessary.
  • Enrichment during P 4 is at a constant concentration, independent of the length of supplementary lighting, because the length of enrichment period is constant (enrichment continues throughout the day even if no light is provided during P 4 ).
  • the end of P 4 is reached, there is again some incentive for trade-off between light and CO 2 concentration, without the need to increase enrichment time.
  • the solution point climbs up curve 3 for a while, until switching to period P 1 (on-peak, night) is justified.
  • the behavior in period P 1 is similar to that in P 2 and for the same reason.
  • curve 4 lights have been on for 24 hours and the only way to reach the target production is to add CO 2 , climbing up curve 1 .
  • the optimal CO 2 concentration is higher for lower ventilation rates.
  • FIG. 7 shows the cost of the solutions of FIG. 6 and, in addition, the cost of adding light only (e.g., when high ventilation rates are required).
  • the change in slope is due to switching from off-peak (9 hours) to on-peak (rest of day) electricity price.
  • Light alone typically cannot efficiently produce the target at very low natural light integral levels, but it is always possible to reach the target by combining light and CO 2 enrichment. Wherever the light-only solution exists, it is the upper bound on the other solutions.
  • the absolute saving from CO 2 enrichment is constant for P 4 , but diminishes towards higher levels of natural light.
  • gaps between segments are where the solutions climb along the curves of FIG. 6 .
  • a few of the solutions are not represented in this figure.
  • G ⁇ ⁇ ⁇ I ⁇ ⁇ x ⁇ ⁇ ⁇ I ⁇ + x - R m [ 47 ] and normalizing with respect to the desired daily growth, G*, [47]
  • a + ⁇ I ⁇ ⁇ x B + ⁇ ⁇ I ⁇ + x - ( C + + 1 ) 0 [ 50 ]
  • Selecting the appropriate values of ⁇ , [I, x] pairs obtained from [42] can be used as data to fit [52].
  • the fitting requires the minimization of
  • ⁇ ⁇ ⁇ ⁇ ⁇ ( A + ⁇ ix B + ⁇ i + x - C + ⁇ ) ⁇ d t [ 55 ]
  • i and x are instantaneous (hourly) values.
  • the value of ⁇ may have to be a guess, perhaps based on the previous 24 hours period. If C is assumed to be zero, [55] reduces to: [55]
  • ⁇ ⁇ ⁇ ⁇ ⁇ ( A + ⁇ ix B + ⁇ i + x ) ⁇ d t , [ 56 ]
  • Light intensity and integral projections for each hour time step may be made using the light control algorithm published by Albright, et al. (2000.)
  • the present invention employs a similar algorithm predicated on controlling supplemental lights to reach a temporally consistent light integral target, but utilizes a daily target that can change hourly, depending on the history of the day and the CO 2 concentration found to be optimum for the predicted ventilation rate for the next hour.
  • the predicted PAR and outdoor air temperature during the near future may be used in a greenhouse energy balance to solve for the expected ventilation rate.
  • Predicting outdoor air temperature one hour ahead of the current hour (the selected time interval) was by extrapolation. If one measures the current and two previous hourly air temperatures, a second order polynomial can be fitted exactly to the three data points and used to extrapolate one time step ahead. A polynomial was used, assuming the temperature trend would continue (the trend and its curvature). This is not always true because sudden temperature changes can occur.
  • FIG. 9 are shown prediction errors for one year of hourly air temperature data for Ithaca, N.Y., U.S.A. Seventy-seven percent of the predictions were within 1 C accuracy, 94% within 2 C accuracy, and 98% within 3 C accuracy.
  • the model and sub-models described above were tested by computer simulation (using hourly weather data for one year) and is estimated to save approximately one-half the lighting energy and nearly forty percent of the operating cost of supplementing the two resources, with no loss of plant production potential when lettuce is the crop of interest.
  • a generic greenhouse was assumed for simulation purposes; representative parameters are listed in Table 3.
  • the model was programmed as an application in Java and one year (1988) of hourly weather data from Ithaca, N.Y., USA, was used for calculations.
  • Table 5 contains comparable data but with supplemental CO 2 enabled. Additional simulations to show the influence of greenhouse light transmissivity and greenhouse air-tightness (averaged air infiltration) were completed and results are in Table 6.
  • Including CO 2 concentration decay when supplementation stopped was important in calculating the virtual PAR integrals of the following hours, particularly during daylight hours when natural light always continued.
  • the decay of CO 2 to ambient required several hours.
  • decay from 1600 ppm, with 0.5 h ⁇ 1 air exchange showed calculated CO 2 hourly concentrations of: 1128, 841, 668, 562, and 499 ppm (at which point supplementation resumed), which is a long decay curve.
  • the simple extrapolation procedure used to predict the next hour's outdoor air temperature showed slightly better efficacy during night when air temperatures are generally more stable. However, the efficacy was relatively constant during the day.
  • Prediction accuracy for the 1988 weather data simulation is shown in FIG. 11 .
  • the outdoor air temperature prediction accuracy is reflected as a function of time of day. Symbols, from bottom to top, represent 0.5, 1.0, 1.5 and 2.0 C errors. The errors are shown (percentages represent how many hours were within each error limit). Accuracy is slightly reduced early in the morning as the air temperature history changes from relatively flat before sunrise, to a sudden jump after sunrise. In such situations, errors were often large positive values for one hour, followed by large negative values the next hour. This is an artifact of the extrapolation procedure.
  • More sophisticated greenhouse air temperature control could be implemented to improve the simple simulation presented here, without deviating from the scope of the present invention.
  • the program was written to keep greenhouse air temperature at the desired set point by using ventilation.
  • the prediction errors where actual outdoor air temperature was one or two degrees above the predicted value would lead to increased ventilation and CO 2 venting.
  • Most greenhouse air temperature control includes a dead band between heating and cooling, with temperature steps of one or two degrees between ventilation/cooling stages. Permitting such temperature drifting would improve the efficacy of the control algorithm.
  • a simple enrichment strategy usable in some embodiments of the invention may be as follows: If the ventilation rate is higher than 0.005 m 3 /(m 2 s), do not enrich. If it is lower, enrich to the maximum permissible concentration (1600 ppm in some embodiments, however no embodiment is limited to any particular maximum permissible concentration).

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US11538099B2 (en) 2016-09-21 2022-12-27 Iunu, Inc. Online data market for automated plant growth input curve scripts
US10791037B2 (en) 2016-09-21 2020-09-29 Iunu, Inc. Reliable transfer of numerous geographically distributed large files to a centralized store
US11783410B2 (en) 2016-09-21 2023-10-10 Iunu, Inc. Online data market for automated plant growth input curve scripts
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US11411841B2 (en) 2016-09-21 2022-08-09 Iunu Inc. Reliable transfer of numerous geographically distributed large files to a centralized store
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US11776050B2 (en) 2016-09-21 2023-10-03 Iunu, Inc. Online data market for automated plant growth input curve scripts
US11582927B1 (en) 2017-12-27 2023-02-21 Serdar Mizrakci System and method for rapidly growing a crop
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